The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for performing learning of privacy protection layers for image recognition services.
Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, computer vision seeks to automate tasks that the human visual system can do intuitively using specially configured computing devices. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. Understanding in this context means the transformation of visual images into descriptions of the world that can interface with other thought processes and elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.
As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems.
Computer vision is used in many applications. For example, computer vision is used in safety systems, such as collision warning systems. Computer vision is also used in various security systems used to monitor residential, business, and industrial environments, traffic monitoring systems, satellite based imaging systems, military systems, and the like.
A generative model is a model for generating all values for a phenomenon, both those that can be observed in the world and “target” variables that can only be computed from those observed. By contrast, discriminative models provide a model only for the target variable(s), generating them by analyzing the observed variables. In simple terms, discriminative models infer outputs based on inputs, while generative models generate both inputs and outputs, typically given some hidden parameters. Generative models are used in machine learning for either modeling data directly (i.e., modeling observations drawn from a probability density function), or as an intermediate step to forming a conditional probability density function. Generative models are typically probabilistic, specifying a joint probability distribution over observation and target (label) values. A conditional distribution can be formed from a generative model through Bayes' rule.
Generative models learn a joint probability distribution p(x, y) of input variables x (the observed data values) and output variables y (determined values). Most unsupervised generative models, such as Boltzmann Machines, Deep Belief Networks, and the like, require complex samplers to train the generative model. However, the recently proposed technique of Generative Adversarial Networks (GANs) repurposes the min/max paradigm from game theory to generate images in an unsupervised manner. The GAN framework comprises a generator and a discriminator, where the generator acts as an adversary and tries to fool the discriminator by producing synthetic images based on a noise input, and the discriminator tries to differentiate synthetic images from true images.